app.py

import faiss
import pickle
import datasets
import numpy as np
import requests
import streamlit as st
from vector_engine.utils import vector_search 
from transformers import AutoModel, AutoTokenizer

from datasets import load_dataset

@st.cache
def read_data(dataset_repo='dhmeltzer/asks_validation_embedded'):
    """Read the data from huggingface."""
    return load_dataset(dataset_repo)

#@st.cache(allow_output_mutation=True)
#def load_bert_model(name="nli-distilbert-base"):
#    """Instantiate a sentence-level DistilBERT model."""
#    return AutoModel.from_pretrained(f'sentence-transformers/{name}')
#
#@st.cache(allow_output_mutation=True)
#def load_tokenizer(name="nli-distilbert-base"):
#    return AutoTokenizer.from_pretrained(f'sentence-transformers/{name}')

@st.cache(allow_output_mutation=True)
def load_faiss_index(path_to_faiss="./faiss_index_small.pickle"):
    """Load and deserialize the Faiss index."""
    with open(path_to_faiss, "rb") as h:
        data = pickle.load(h)
    return faiss.deserialize_index(data)

def main():
    # Load data and models
    data = read_data()
    #model = load_bert_model()
    #tok = load_tokenizer()
    faiss_index = load_faiss_index()

    import requests

    model_id="sentence-transformers/nli-distilbert-base"
    
    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
    headers = {"Authorization": "Bearer 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"}

    def query(texts):
        response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
        return response.json()


    st.title("Vector-based searches with Sentence Transformers and Faiss")

    # User search
    user_input = st.text_area("Search box", "ELI5 Dataset")

    # Filters
    st.sidebar.markdown("**Filters**")

    filter_scores = st.sidebar.slider("Citations", 0, 250, 0)
    num_results = st.sidebar.slider("Number of search results", 1, 50, 1)

    vector = query([user_input])
    # Fetch results
    if user_input:
        # Get paper IDs
        _, I = faiss_index.search(np.array(vector).astype("float32"), k=num_results)
        #D, I = vector_search([user_input],tok, model, faiss_index, num_results)
        
        # Slice data on year
        #frame = data[
        #    (data.scores >= filter_scores)
        #]
        
        frame = data
        st.write(user_input)
        # Get individual results
        for id_ in I.flatten().tolist():
            f = frame[id_]
            #if id_ in set(frame.id):
            #    f = frame[(frame.id == id_)]
            #else:
            #    continue

            st.write(
                f"""**{f['title']}**  
            **text**: {f['selftext']}  
            """
            )


if __name__ == "__main__":
    main()